Anomalib vs Cisco Catalyst Center
Anomalib
github.comCisco Catalyst Center
cisco.comSide by side
| Figure | Anomalib | Cisco Catalyst Center |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 2 |
| Spec score · Anomaly Detection Software | 9.1 (#1) | 7.7 (#7) |
| Deployment | Inference options include Torch, Lightning, Gradio, and OpenVINO. | Cisco describes hardware and virtual appliance deployments, including current guides for VMware ESXi and Ubuntu KVM. |
| Purpose | Anomalib is a deep learning library for benchmarking, developing, and deploying anomaly detection algorithms, with a focus on detecting or localizing anomalies in images and videos. | Cisco Catalyst Center is a centralized network controller and management dashboard for enterprise network operations. |
| Security | The project documents continuous security scanning with CodeQL, Semgrep, Bandit, Zizmor, Trivy, and Dependabot, and directs vulnerability reports to Intel's vulnerability handling guidelines. | Catalyst Center supports FIPS 140-2-compliant cryptography modules, and Cisco recommends deploying it behind a firewall on a private internal network. |
Plans
Cisco Catalyst Center
- Cisco DNA AdvantagePriced on request
- Cisco DNA EssentialsPriced on request
In their own words
Anomalib
Anomalib is a free, Apache-2.0-licensed deep learning library for developing, benchmarking and deploying anomaly detection algorithms, especially for images and videos. Its modular Python API and command-line interface support training, inference and benchmarking, and the project includes ready-to-use algorithms and benchmark datasets. Model implementations are based on Lightning; inference options include Torch, Lightning, Gradio and OpenVINO. Most models can be exported to OpenVINO Intermediate Representation for accelerated inference on Intel hardware. Installation choices include CPU, NVIDIA CUDA on Linux or Windows, AMD ROCm on Linux and Intel XPU on Linux. Documented experiment-tracking integrations include Weights & Biases, Comet.ml and TensorBoard through PyTorch Lightning loggers. Anomalib Studio adds a low/no-code web application that accepts USB or IP cameras or image folders and can send output to industrial pipelines through ROS messages or MQTT. Studio is offered as a Docker container or standalone application, but is a pre-release under active development; features may change and functionality may be incomplete or unstable. Intel GPU training currently supports one GPU.
Cisco Catalyst Center
Cisco Catalyst Center has no write-up on Specifiction yet; its figures are above.